Create app.py

app.py

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+import streamlit as st
+import pickle
+from sentence_transformers import CrossEncoder,SentenceTransformer
+with open('eiga_docs.pkl', 'rb') as file:
+    documents=pickle.load( file)
+#with open('index.pkl', 'rb') as file:
+#    index=pickle.load( file)
+#documents =[ i["title"]+" "+i["text"] for i in documentt]
+#bi_encoder = SentenceTransformer('all-mpnet-base-v2',device="cpu",)
+
+# Load a cross-encoder model for re-ranking
+cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+
+# Streamlit app
+#st.title("Information Retrieval System")
+
+#query = st.text_input("Enter your query:")
+
+
+#if query:
+    # Encode the query using the bi-encoder
+    #query_embedding = bi_encoder.encode([query])
+
+    # Retrieve top-k documents using FAISS
+    #k = 30  # Number of documents to retrieve
+    #D, I = index.search(query_embedding, k)
+    
+    #Collect the retrieved documents
+    #retrieved_docs = [documents[i] for i in I[0]]
+    
+    #Re-rank the retrieved documents using the cross-encoder
+    #cross_inp = [[query, doc["title"]+" "+doc["text"]] for doc in documents]
+    #scores = cross_encoder.predict(cross_inp)
+    
+    # Sort the documents by score
+    #sorted_docs = [documents[doc] for _, doc in sorted(zip(scores, range(len(documents))), reverse=True)][:10]
+
+    # Display the results
+    #st.write("Top documents:")
+    #for i, doc in enumerate(sorted_docs):
+    #    st.write(f"Result {i+1}. Title: {doc['title']}")
+    #    st.write(f"Text : {doc['text']}")
+    #    st.write(f"Lien de telechargment : {doc['doc_url']}")
+    #    st.markdown("""---""")
+
+
+
+# Function to display results for a given page
+def display_results(sorted_docs, page_number, results_per_page):
+    start_index = (page_number - 1) * results_per_page
+    end_index = min(page_number * results_per_page, len(sorted_docs))
+    for i, doc in enumerate(sorted_docs[start_index:end_index], start=start_index):
+        st.write(f"**Result {i+1}.** Title: {doc['title']}")
+        st.write(f"Text : {doc['text']}")
+        st.write(f"Link : {doc['doc_url']}")
+        st.markdown("""---""")
+
+
+
+# Function to perform search and return sorted documents
+def perform_search(query):
+    if query:
+        cross_inp = [[query, doc["title"] + " " + doc["text"]] for doc in documents]
+        scores = cross_encoder.predict(cross_inp)
+
+        # Sort the documents by score
+        sorted_docs = [documents[doc] for _, doc in sorted(zip(scores, range(len(documents))), reverse=True)]
+        return sorted_docs
+    else:
+        return []
+
+# Main Streamlit app
+def main():
+    st.sidebar.title("Pagination")
+    results_per_page = st.sidebar.number_input("Results per page", min_value=1, max_value=10, value=5)
+    total_pages = (len(documents) + results_per_page - 1) // results_per_page
+    page_number = st.sidebar.number_input("Page Number", min_value=1, max_value=total_pages, value=1)
+    st.sidebar.write(f"Current Page Number: {page_number}")
+    st.sidebar.write( f"Totale Pages :{total_pages}")
+    #print(results_per_page,total_pages,page_number)
+   
+    st.title("Information Retrieval System")
+    query = st.text_input("Enter your question:")
+
+   
+
+    if st.button("Search") or query:
+        sorted_docs = perform_search(query)
+        st.session_state.sorted_docs = sorted_docs
+        
+    else:
+        sorted_docs = st.session_state.get("sorted_docs", [])
+
+
+
+    if sorted_docs:
+        display_results(st.session_state.sorted_docs, page_number, results_per_page)
+        st.write(f"Current Page Number: {page_number}")
+        
+
+
+
+# Run the app
+if __name__ == "__main__":
+    main()